SoroushVahidi/scidocs huggingface dataset:
On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?
(2017)

The Philosophy of Blockchain Technology is concerned, among other things, with its ontology, how might it be characterised, how is it being created, implemented, and adopted, how does it operate in the world, and how does it evolve over time. Here, we concentrate on whether Bitcoin/blockchain can be considered a complex system and, if so, whether a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-FlashCrash-type of chaotic regime, with catastrophic consequences for financial systems based on it. This paper starts by enhancing the relevant details of Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end-users (payers and payees), capital gain seekers, miners, full nodes maintainers, and developers, and their interactions. Secondly, the Information Theory of Complex Systems is briefly discussed for later use. Finally, blockchain is investigated with the help of Crutchfield‟s Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that blockchain may be considered algorithmically complicated, but hardly a complex system and unlikely to enter a chaotic regime.
WiStep: Device-free Step Counting with WiFi Signals
(2017)

Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.
Word Attention for Sequence to Sequence Text Understanding
(2018)

Attention mechanism has been a key component in Recurrent Neural Networks (RNNs) based sequence to sequence learning framework, which has been adopted in many text understanding tasks, such as neural machine translation and abstractive summarization. In these tasks, the attention mechanism models how important each part of the source sentence is to generate a target side word. To compute such importance scores, the attention mechanism summarizes the source side information in the encoder RNN hidden states (i.e., ht), and then builds a context vector for a target side word upon a subsequence representation of the source sentence, since ht actually summarizes the information of the subsequence containing the first t-th words in the source sentence. We in this paper, show that an additional attention mechanism called word attention, that builds itself upon word level representations, significantly enhances the performance of sequence to sequence learning. Our word attention can enrich the source side contextual representation by directly promoting the clean word level information in each step. Furthermore, we propose to use contextual gates to dynamically combine the subsequence level and word level contextual information. Experimental results on abstractive summarization and neural machine translation show that word attention significantly improve over strong baselines. In particular, we achieve the state-of-the-art result on WMT’14 English-French translation task with 12M training data.
Neither Snow Nor Rain Nor MITM...: An Empirical Analysis of Email Delivery Security
(2015)

The SMTP protocol is responsible for carrying some of users' most intimate communication, but like other Internet protocols, authentication and confidentiality were added only as an afterthought. In this work, we present the first report on global adoption rates of SMTP security extensions, including: STARTTLS, SPF, DKIM, and DMARC. We present data from two perspectives: SMTP server configurations for the Alexa Top Million domains, and over a year of SMTP connections to and from Gmail. We find that the top mail providers (e.g., Gmail, Yahoo, and Outlook) all proactively encrypt and authenticate messages. However, these best practices have yet to reach widespread adoption in a long tail of over 700,000 SMTP servers, of which only 35% successfully configure encryption, and 1.1% specify a DMARC authentication policy. This security patchwork---paired with SMTP policies that favor failing open to allow gradual deployment---exposes users to attackers who downgrade TLS connections in favor of cleartext and who falsify MX records to reroute messages. We present evidence of such attacks in the wild, highlighting seven countries where more than 20% of inbound Gmail messages arrive in cleartext due to network attackers.
Fake News Headline Classification using Neural Networks with Attention
(2017)

The Fake News Challenge is a competition to classify articles as agreeing, disagreeing, discussing, or having no relation to their headlines. The competition provides an annotated dataset (FNC-1) for training supervised models as well as a baseline model to compare against. We cast our fake news problem as a textual entailment task by using multiple Bidirectional LSTMs and an attention mechanism to predict the entailment of the articles to their paired headlines. To tune our model, we adjust a multilayer perceptron predictor layer’s width as well as the amount of dropout and regularization to control overfitting. We evaluate the effectiveness of our model against the baseline and compare to implementations of solutions to the same FNC-1 stance classification task.
A Fusion Approach for Efficient Human Skin Detection
(2012)

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.
Stemming Algorithms: A Comparative Study and their Analysis
(2012)

Stemming is an approach used to reduce a word to its stem or root form and is used widely in information retrieval tasks to increase the recall rate and give us most relevant results. There are number of ways to perform stemming ranging from manual to automatic methods, from language specific to language independent each having its own advantage over the other. This paper represents a comparative study of various available stemming alternatives widely used to enhance the effectiveness and efficiency of information retrieval.
Simulation and analysis of DQ frame and P+Resonant controls for voltage source inverter to distributed generation
(2009)

An inverter control design in DQ reference frame takes 3-phase sinusoidal signals and converts them to the DQ-frame to make a 2-phase based DC variable control. The error is calculated by subtracting the feedback from reference and then feedding it to PI controllers. The outputs of PI controllers are of the voltage form and transferred back to the ABC frame and fed to SPWM modules. P+Resonant controllers suggest establishing the control strategy in stationary (alpha-beta) frame. In this frame the axis are fix and three phase variables are mapped in a rotating vector with the same angular speed of the original signals. The P+Resonant controller has zero steady state error at the resonant frequency. This paper explains how to apply DQ-frame and P+Resonant for inverter controller design for grid connected single-phase Voltage Source Inverters (VSI). To evaluate the effectiveness of both control techniques, it is also presented simulated results with application of these controllers.
The effect of Bikram yoga on arterial stiffness in young and older adults.
(2013)

BACKGROUND AND OBJECTIVE
Bikram yoga is the most popular form of hot yoga, despite the limited information available on its cardiovascular benefits. This study sought to determine the effect of Bikram yoga on arterial stiffness and insulin resistance in young and older adults.


METHODS
Twenty-four young (mean age±standard deviation, 30±1 years) and 18 middle-aged and older (mean age, 53±2 years) adults completed an 8-week Bikram yoga intervention. Bikram yoga classes were performed for 90 minutes per session, three times per week, in a room heated to 40.5°C with 40%--60% relative humidity.


RESULTS
Body mass, body fat percentage, blood pressure, and fasting blood glucose and triglyceride concentrations did not significantly change as a result of the intervention in either the young or the older group. Trunk flexibility, as measured by the sit-and-reach test, increased in both groups (p<0.01). Total (p<0.05) and low-density lipoprotein cholesterol (p<0.05) levels, plasma insulin concentrations (p<0.01), and scores on the homeostatic model of the assessment of insulin resistance (p<0.01) decreased in older adults, whereas total and high-density lipoprotein cholesterol concentrations were reduced in young adults (all p<0.05). Carotid artery compliance (p<0.05) was increased and β-stiffness index decreased in young (p<0.05) but not in older adults. Carotid pulse pressure did not significantly change in either group.


CONCLUSION
A relatively short-term Bikram yoga intervention improved arterial stiffness in young but not older adults and significantly reduced insulin resistance index in older but not young adults.
Spectral Hashing
(2008)

Semantic hashing[1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of manifolds, we show how to efficiently calculate the code of a novel datapoint. Taken together, both learning the code and applying it to a novel point are extremely simple. Our experiments show that our codes outperform the state-of-the art.
Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session
(2015)

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.
Anterior prefrontal function and the limits of human decision-making.
(2007)

The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.
Non linear hybrid watermarking for High Dynamic Range images
(2013)

The present work explores the use of a specific method, called non linear hybrid watermarking to watermark High Dynamic Range (HDR) images. The non linear hybrid technique combines both additive and multiplicative watermark embedding and is based on a square root embedding equation. We evaluate the robustness and objective quality of HDR tone mapped watermarked images on two different HDR databases. The experimentations show that the watermark is imperceptible and successfully survives tone mapping attack.
Autonomous Intersection Management: A Heuristic Approach
(2018)

Self-driving cars do not sound like a part of science fiction movies anymore. With most of the automobile giants committed to launching their autonomous cars as soon as possible, it is expected that self-driving cars will hit the road in a very short time. Increasing the penetration factor of autonomous vehicles will need algorithms to control them in an efficient way in different scenarios. One such scenario is intersection management (IM). We propose an intuitive heuristic to resolve space–time conflicts in vehicle’s trip, enabling vehicles to cross the intersection safely, and with minimum delay. The intersection is modeled as a group of conflict points where intersecting internal links cross each other. For safe and efficient scheduling of vehicles, heuristics are proposed separately for: 1) entering in approach lane; 2) safe traversal in the approach lane; 3) safe and efficient crossing of intersection; and (4) safe departure along depart lane. Vehicle scheduling is done using a three-leveled heuristic in which each level serves a distinct purpose and together guarantee collision-free passing of vehicles. Performance evaluation of the proposed scheme is done in Simulation of Urban MObility simulator and a comparative analysis is done with three other IM schemes. Experiments show that the proposed scheme gives a smaller average trip delay of vehicles compared with other IM schemes.
A Hybrid Document Feature Extraction Method Using Latent Dirichlet Allocation and Word2Vec
(2016)

Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this paper, we propose a hybrid approach to extract features from documents with bag-of-distances in a semantic space. By using both Word2Vec and LDA, our hybrid method not only generates the relationships between documents and topics, but also integrates the contextual relationships among words. Experimental results indicate that document features generated by our hybrid method are useful to improve classification performance by consolidating both global and local relationships.
Exploitation of spectral redundancy in cyclostationary signals
(1991)

It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described.<<ETX>>
Decentralized Reactive Power Sharing and Frequency Restoration in Islanded Microgrid
(2017)

P-f and Q-V droop methods are the most common decentralized control methods in islanded microgrid. Although with the P-f droop method, an accurate active power sharing can be achieved among distributed energy resources (DERs), by Q-V droop, the reactive power sharing among DERs often deteriorates due to its highly dependence on the power line impedances and the local load. Variation of frequency and voltage by load changes is another challenge in droop control method. In this paper, a new autonomous control method is proposed to share reactive power among DERs accurately and restore frequency of a microgrid. The proposed method does not require any communication link and so maintains reliability and simplicity of the network. The synchronizing among DERs is obtained by the load change detection, which is accomplished by wavelet transform. The method operation principle is explained and analyzed. Simulation results are presented to validate the effectiveness of the proposed method.
Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features
(2014)

Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer’s disease (AD) diagnosis. This is a normal “knowledge diffusion” process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: “Bordeaux dataset”. The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and “Bordeaux dataset” respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.
Knowledge Representation for Robots through Human-Robot Interaction
(2013)

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing “knowledgeable” robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.
A DUAL-BAND CIRCULARLY POLARIZED STUB LOADED MICROSTRIP PATCH ANTENNA FOR GPS APPLICATIONS
(2009)

A single-feed low-profile and easy to fabricate circularly polarized microstrip patch antenna has been developed for GPS applications. For dual frequency operation, four slots are etched near edges of the patch and a crossed slot etched in the center for generating circular polarization. In order to reducing the frequency ratio of two frequency bands of the antenna, the patch is loaded by four short circuit microstrip stubs. The paper reports several simulation results that confirm the desired characteristics of the antenna. Using stub loading, the frequency ratio of two bands of the antenna can be, even, reduced to 1.1.